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Mastery (10) |
Incomplete (0) |
Question 1: Definitions |
The student correctly defined each of the terms and included mathematical expressions or illustration if available in the text or the Time Series Notebook |
The student did not provide a correct definition for one or more of the terms. |
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Mastery (10) |
Incomplete (0) |
Question 2a: OLS linear trend |
Students estimate the linear model using OLS and provide well-commented code. Results are presented clearly in a professionally formatted table. |
Students struggle to estimate the linear model using OLS or provide poorly commented code. Results may be unclear or inaccurately presented in the table format. |
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Mastery (5) |
Incomplete (0) |
Question 2b: Autocorrelation plots |
Students create clear plots with appropriate labeling and provide well-commented code. |
Plots have insufficient clarity, labeling, or code comments, hindering the analysis of autocorrelation. |
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Mastery (10) |
Incomplete (0) |
Question 2c: Residual AR(p) modeling |
Students fit residuals appropriately, selecting order based on correlogram and partial correlogram. They also include statistical evidence using R statistical tests of AR(p) model fit. They provide well-commented code and present their results clearly |
Submissions struggle to fit residuals or select the order of autoregressive model using plots and statistical evidence |
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Mastery (15) |
Incomplete (0) |
Question 2d: GLS linear trend AR(p) errors |
Students accurately estimate the linear model using GLS using their results in part c. Results are presented clearly in a professionally formatted table that includes a comparison of the GLS and OLS point estimates, standard errors, and confidence intervals. |
Submissions don’t implement the GLS algorithm correctly. Students don’t display the results professionally, or they don’t include a comparison to OLS results. |
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Mastery (15) |
Incomplete (0) |
Question 2e: Autocorrelation Bias |
Students provide clear analysis of autocorrelation bias and its forecasting implications. They point out the connection between standard errors and forecasting confidence bands. |
Students may provide incomplete or inaccurate analysis of autocorrelation bias or its forecasting implications, lacking clarity or depth in discussion of its importance. |
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Mastery (10) |
Incomplete (0) |
Question 3a: OLS additive seasonal indicator variables |
Students accurately estimate the linear model using OLS, including seasonal indicator variables, and provide well-commented code. Results are presented clearly in a professionally formatted table. |
Students struggle to estimate the linear model using OLS or provide poorly commented code. Results may be unclear or inaccurately presented in the table format. |
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Mastery (10) |
Incomplete (0) |
Question 3b: Coefficient interpretation |
Students provide a correct interpretation of the coefficient for January (including the correct units). and relate to the effect on the Women’s Clothing Retail Sales. |
Interpretation of the coefficient for January is incomplete, inaccurate, or unclear, lacking a direct connection to its effect on the Women’s Clothing Retail Sales. |
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Mastery (10) |
Incomplete (0) |
Question 3c: Perfect Colinearity |
Students provide a clear interpretation of the intercept estimate in the context of the Women’s Clothing Retail Sales data, considering how it relates to the additive seasonal indicator variables |
Interpretation of the intercept estimate may be incomplete, inaccurate, or unclear. It doesn’t make clear the perfect colinearity problem and the correct interpretation of the dropped variable. |
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Mastery (10) |
Incomplete (0) |
Question 4d: Forecast |
Students accurately make the five-year forecast using the estimated model, including 95% confidence bands in their plot. |
Students encounter difficulties in making the five-year forecast or don’t include the forecast plot. Code may be poorly commented or the inclusion of confidence bands may be omitted.
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| Total Points |
105 |
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